from __future__ import print_function
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt


def add_layer(inputs, in_size, out_size, n_layer, activation_function=None):
    layer_name = 'layer%s' % n_layer
    with tf.name_scope('layer'):
        with tf.name_scope('weights'):
            Weights = tf.Variable(tf.random_normal([in_size, out_size]), name='W')
            tf.summary.histogram(layer_name + '/weights', Weights)
        with tf.name_scope('biases'):
            biases = tf.Variable(tf.zeros([1, out_size]) + 0.1, name='b')
            tf.summary.histogram(layer_name + '/biases', biases)
        with tf.name_scope('Wx_plus_b'):
            Wx_plus_b = tf.add(tf.matmul(inputs, Weights), biases)
        if activation_function is None:
            outputs = Wx_plus_b
        else:
            outputs = activation_function(Wx_plus_b, )
        tf.summary.histogram(layer_name + '/outputs', outputs)
        return outputs


x_data = np.linspace(-1, 1, 300, dtype=np.float32)[:, np.newaxis]
noise = np.random.normal(0, 0.05, x_data.shape)
y_data = np.square(x_data) - 0.5 + noise

with tf.name_scope('inputs'):
    xs = tf.placeholder(tf.float32, [None, 1])
    ys = tf.placeholder(tf.float32, [None, 1])

L1 = add_layer(xs, 1, 10, n_layer=1, activation_function=tf.nn.relu)
prediction = add_layer(L1, 10, 1, n_layer=2, activation_function=None)

with tf.name_scope('loss'):
    loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction), reduction_indices=[1]))
    tf.summary.scalar('loss', loss)

with tf.name_scope('train'):
    train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)


sess = tf.Session()
merged = tf.summary.merge_all()
writer = tf.summary.FileWriter("logs/", sess.graph)

sess.run(tf.global_variables_initializer())
# fig = plt.figure()
# ax = fig.add_subplot(1, 1, 1)
# ax.scatter(x_data, y_data)
# plt.ion()
# plt.show()
#
for i in range(1000):
    sess.run(train_step, feed_dict={xs: x_data, ys: y_data})
    i1 = 50
    if i % i1 == 0:
        result = sess.run(merged, feed_dict={xs: x_data, ys: y_data})
        writer.add_summary(result, i)
# try:
#             ax.lines.remove(lines[0])
#         except Exception:
#             pass
#         prediction_value = sess.run(prediction, feed_dict={xs: x_data})
#         lines = ax.plot(x_data, prediction_value, 'r-', lw=5)
#         plt.pause(1)
#         # print(sess.run(loss, feed_dict={xs: x_data, ys: y_data}))
